/*
 * Copyright 2016 University of Basel, Graphics and Vision Research Group
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package scalismo.sampling.algorithms

import scalismo.sampling.{MarkovChain, ProposalGenerator}

/** Markov chain which draws the next sample from a proposal distribution */
class ForwardChain[A](proposalGenerator: ProposalGenerator[A]) extends MarkovChain[A] {

  /** next sample in chain */
  override def next(current: A): A = proposalGenerator.propose(current)
}

object ForwardChain {
  def apply[A](proposalGenerator: ProposalGenerator[A]) = new ForwardChain[A](proposalGenerator)
}
